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The validity of diagnostic algorithms to identify asthma patients in healthcare administrative databases: a systematic literature review

期刊

JOURNAL OF ASTHMA
卷 59, 期 1, 页码 152-168

出版社

TAYLOR & FRANCIS LTD
DOI: 10.1080/02770903.2020.1827425

关键词

Asthma; validation; diagnostic algorithms; administrative databases

资金

  1. Quebec SPOR support Unit - Canadian Institutes of Health Research (CIHR)
  2. Ministere de la sante et des services sociaux du Quebec
  3. Fonds de recherche du Quebec-Sante

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This study aimed to evaluate the validity of algorithms using healthcare administrative databases to identify asthma patients. The systematic review found that algorithms that combined asthma diagnostic codes with medication data were effective in identifying asthma patients in both adult and pediatric populations. However, further research is needed to confirm these findings and determine the impact of asthma medications on validity.
Objectives To review the available evidence supporting the validity of algorithms to identify asthma patients in healthcare administrative databases. Methods A systematic literature search was conducted on multiple databases from inception to March 2020 to identify studies that reported the validity of case-finding asthma algorithms applied to healthcare administrative data. Following an initial screening of abstracts, two investigators independently assessed the full text of studies which met the pre-determined eligibility criteria. Data on study population and algorithm characteristics were extracted. A revised version of the Quality Assessment of Diagnostic Accuracy Studies tool was used to evaluate the risk of bias and generalizability of studies. Results A total of 20 studies met the eligibility criteria. Algorithms which incorporated >= 1 diagnostic code for asthma over a 1-year period appeared to be valid in both adult and pediatric populations (sensitivity >= 85%; specificity >= 89%; PPV >= 70%). The validity was enhanced when: (1) the time frame to capture asthma cases was increased to two years; (2) >= 2 asthma diagnostic codes were considered; and (3) when diagnoses were recorded by a pulmonologist. Algorithms which integrated pharmacy claims data appeared to correctly identify asthma patients; however, the extent to which asthma medications can improve the validity remains unclear. The quality of several studies was high, although disease progression bias and biases related to self-reported data was observed in some studies. Conclusions Healthcare administrative databases are adequate sources to identify asthma patients. More restrictive definitions based on both asthma diagnoses and asthma medications may enhance validity, although further research is required to confirm this hypothesis.

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